A geometrical-based throughput bound analysis for device-to-device communications in cellular networks
Bibliographic record
Abstract
Abstract—Device-to-device (D2D) communications in cellular networks are promising technologies for improving network throughput, spectrum efficiency, and transmission delay. In this paper, we first introduce the concept of guard distance to explore a proper system model for enabling multiple concurrent D2D pairs in the same cell. Considering the Signal to Interference Ratio (SIR) requirements for both macro-cell and D2D communications, a geometrical method is proposed to obtain the guard distances from a D2D user equipment (DUE) to the base station (BS), to the trans-mitting cellular user equipment (CUE), and to other communicat-ing D2D pairs, respectively, when the uplink resource is reused. By utilizing the guard distances, we then derive the bounds of the maximum throughput improvement provided by D2D communi-cations in a cell. Extensive simulations are conducted to demon-strate the impact of different parameters on the optimal maximum throughput. We believe that the obtained results can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications. Index Terms—Device-to-device (D2D) communications, uplink reuse, throughput, guard distances, circle packing. I.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".